9 papers
The Stability of Online Algorithms in Performative Prediction
Gabriele Farina, Juan Carlos Perdomo
The use of algorithmic predictions in decision-making leads to a feedback loop where the models we deploy actively influence the data distributions we see, and later use to retrain…
An Efficient Black-Box Reduction from Online Learning to Multicalibration, and a New Route to -Regret Minimization
Gabriele Farina, Juan Carlos Perdomo
We give a Gordon-Greenwald-Marks (GGM) style black-box reduction from online learning to online multicalibration. Concretely, we show that to achieve high-dimensional multicalibrat…
Defensive Generation
Gabriele Farina, Juan Carlos Perdomo
We study the problem of efficiently producing, in an online fashion, generative models of scalar, multiclass, and vector-valued outcomes that cannot be falsified on the basis of th…
In Defense of Defensive Forecasting
Juan Carlos Perdomo, Benjamin Recht
This tutorial provides a survey of algorithms for Defensive Forecasting, where predictions are derived not by prognostication but by correcting past mistakes. Pioneered by Vovk, De…
The Value of Prediction in Identifying the Worst-Off
Unai Fischer-Abaigar, Christoph Kern, Juan Carlos Perdomo
Machine learning is increasingly used in government programs to identify and support the most vulnerable individuals, prioritizing assistance for those at greatest risk over optimi…
Revisiting the Predictability of Performative, Social Events
Juan C. Perdomo
Social predictions do not passively describe the future; they actively shape it. They inform actions and change individual expectations in ways that influence the likelihood of the…